2,254 research outputs found

    Egg shape changes at the theropod–bird transition, and a morphometric study of amniote eggs

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    The eggs of amniotes exhibit a remarkable variety of shapes, from spherical to elongate and from symmetrical to asymmetrical. We examine eggshell geometry in a diverse sample of fossil and living amniotes using geometric morphometrics and linear measurements. Our goal is to quantify patterns of morphospace occupation and shape variation in the eggs of recent through to Mesozoic birds (neornithe plus non-neornithe avialans), as well as in eggs attributed to non-avialan theropods. In most amniotes, eggs show signicant deviation from sphericity, but departure from symmetry around the equatorial axis is mostly conned to theropods and birds. Mesozoic bird eggs differ signicantly from extant bird eggs, but extinct Cenozoic bird eggs do not. This suggests that the range of egg shapes in extant birds had already been attained in the Cenozoic. We conclude with a discussion of possible biological factors imparting variation to egg shapes during their formation in the oviduct

    Determination of the characteristic directions of lossless linear optical elements

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    We show that the problem of finding the primary and secondary characteristic directions of a linear lossless optical element can be reformulated in terms of an eigenvalue problem related to the unimodular factor of the transfer matrix of the optical device. This formulation makes any actual computation of the characteristic directions amenable to pre-implemented numerical routines, thereby facilitating the decomposition of the transfer matrix into equivalent linear retarders and rotators according to the related Poincare equivalence theorem. The method is expected to be useful whenever the inverse problem of reconstruction of the internal state of a transparent medium from optical data obtained by tomographical methods is an issue.Comment: Replaced with extended version as published in JM

    A Horizon Scan of research priorities to inform policies aimed at reducing the harm of plastic pollution to biota

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    Plastic pollution in the oceans is a priority environmental issue. The recent increase in research on the topic, coupled with growing public awareness, has catalyzed policymakers around the world to identify and implement solutions that minimize the harm caused by plastic pollution. To aid and coordinate these efforts, we surveyed experts with scientific experience identified through their peer-reviewed publications. We asked experts about the most pressing research questions relating to how biota interact with plastic pollution that in turn can inform policy decisions and research agendas to best contribute to understanding and reducing the harm of plastic pollution to biota. We used a modified Horizon Scan method that first used a subgroup of experts to generate 46 research questions on aquatic biota and plastics, and then conducted an online survey of researchers globally to prioritize questions in terms of their importance to inform policy development. One hundred and fifteen experts from 29 countries ranked research questions in six themes. The questions were ranked by urgency, indicating which research should be addressed immediately, which can be addressed later, and which are of limited relevance to inform action on plastics as an environmental pollutant. We found that questions relating to the following four themes were the most commonly top-ranked research priorities: (i) sources, circulation and distribution of plastics, (ii) type of harm from plastics, (iii) detection of ingested plastics and the associated problems, and (iv) related economies and policy to ingested plastics. While there are many research questions on the topic of impacts of plastic pollution on biota that could be funded and investigated, our results focus collective priorities in terms of research that experts believe will inform effective policy and on-the-ground conservation.Š 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/

    Type-2 diabetes mellitus diagnosis from time series clinical data using deep learning models.

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    Clinical data is usually observed and recorded at irregular intervals and includes: evaluations, treatments, vital sign and lab test results. These provide an invaluable source of information to help diagnose and understand medical conditions. In this work, we introduce the largest patient records dataset in diabetes research: King Abdullah International Research Centre Diabetes (KAIMRCD) which includes over 14k patient data. KAIMRCD contains detailed information about the patient’s visit and have been labelled against T2DM by clinicians. The data is processed as time series and then investigated using temporal predictive Deep Learning models with the goal of diagnosing Type 2 Diabetes Mellitus (T2DM). Long Short-Term Memory (LSTM) and Gated-Recurrent Unit (GRU) are trained on KAIMRCD and are demonstrated here to outperform classical machine learning approaches in the literature with over 97% accuracy

    From a Conceptual Model to a Knowledge Graph for Genomic Datasets

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    Data access at genomic repositories is problematic, as data is described by heterogeneous and hardly comparable metadata. We previously introduced a unified conceptual schema, collected metadata in a single repository and provided classical search methods upon them. We here propose a new paradigm to support semantic search of integrated genomic metadata, based on the Genomic Knowledge Graph, a semantic graph of genomic terms and concepts, which combines the original information provided by each source with curated terminological content from specialized ontologies. Commercial knowledge-assisted search is designed for transparently supporting keyword-based search without explaining inferences; in biology, inference understanding is instead critical. For this reason, we propose a graph-based visual search for data exploration; some expert users can navigate the semantic graph along the conceptual schema, enriched with simple forms of homonyms and term hierarchies, thus understanding the semantic reasoning behind query results

    Ancient Migratory Events in the Middle East: New Clues from the Y-Chromosome Variation of Modern Iranians

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    Knowledge of high resolution Y-chromosome haplogroup diversification within Iran provides important geographic context regarding the spread and compartmentalization of male lineages in the Middle East and southwestern Asia. At present, the Iranian population is characterized by an extraordinary mix of different ethnic groups speaking a variety of Indo-Iranian, Semitic and Turkic languages. Despite these features, only few studies have investigated the multiethnic components of the Iranian gene pool. In this survey 938 Iranian male DNAs belonging to 15 ethnic groups from 14 Iranian provinces were analyzed for 84 Y-chromosome biallelic markers and 10 STRs. The results show an autochthonous but non-homogeneous ancient background mainly composed by J2a sub-clades with different external contributions. The phylogeography of the main haplogroups allowed identifying post-glacial and Neolithic expansions toward western Eurasia but also recent movements towards the Iranian region from western Eurasia (R1b-L23), Central Asia (Q-M25), Asia Minor (J2a-M92) and southern Mesopotamia (J1-Page08). In spite of the presence of important geographic barriers (Zagros and Alborz mountain ranges, and the Dasht-e Kavir and Dash-e Lut deserts) which may have limited gene flow, AMOVA analysis revealed that language, in addition to geography, has played an important role in shaping the nowadays Iranian gene pool. Overall, this study provides a portrait of the Y-chromosomal variation in Iran, useful for depicting a more comprehensive history of the peoples of this area as well as for reconstructing ancient migration routes. In addition, our results evidence the important role of the Iranian plateau as source and recipient of gene flow between culturally and genetically distinct population
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